通过多聚类融合检测推荐系统中的局部与全局异常用户
ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System
- 基于用户-用户矩阵，融合多种聚类算法识别异常行为
- 同时检测局部和全局异常，提升检测覆盖度
- 适用于需要提升推荐系统鲁棒性的场景
推荐系统旨在根据用户偏好推荐项目，帮助用户在海量信息中导航。面对信息过载，异常检测已成为推荐系统的关键研究方向，涉及识别用户行为中的异常或可疑模式。然而，现有研究面临算法普适性不足、用户选择困难及优化欠缺等问题。本文提出一种新方法，通过融合多种聚类算法解决上述挑战。具体而言，利用基于用户-用户矩阵的聚类技术检测异常用户，可有效识别系统中的可疑用户。本方法同时检测局部与全局异常，确保分析全面。实验结果表明，该方法显著提升了推荐系统中异常检测的准确性。
原文摘要 · Abstract (English)
Recommender systems are designed to suggest items based on user preferences, helping users navigate the vast amount of information available on the internet. Given the overwhelming content, outlier detection has emerged as a key research area in recommender systems. It involves identifying unusual or suspicious patterns in user behavior. However, existing studies in this field face several challenges, including the limited universality of algorithms, difficulties in selecting users, and a lack of optimization. In this paper, we propose an approach that addresses these challenges by employing various clustering algorithms. Specifically, we utilize a user-user matrix-based clustering technique to detect outliers. By constructing a user-user matrix, we can identify suspicious users in the system. Both local and global outliers are detected to ensure comprehensive analysis. Our experimental results demonstrate that this approach significantly improves the accuracy of outlier detection in recommender systems.
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